Title |
A Syst-OMICS Approach to Ensuring Food Safety and Reducing the Economic Burden of Salmonellosis
|
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Published in |
Frontiers in Microbiology, June 2017
|
DOI | 10.3389/fmicb.2017.00996 |
Pubmed ID | |
Authors |
Jean-Guillaume Emond-Rheault, Julie Jeukens, Luca Freschi, Irena Kukavica-Ibrulj, Brian Boyle, Marie-Josée Dupont, Anna Colavecchio, Virginie Barrere, Brigitte Cadieux, Gitanjali Arya, Sadjia Bekal, Chrystal Berry, Elton Burnett, Camille Cavestri, Travis K. Chapin, Alanna Crouse, France Daigle, Michelle D. Danyluk, Pascal Delaquis, Ken Dewar, Florence Doualla-Bell, Ismail Fliss, Karen Fong, Eric Fournier, Eelco Franz, Rafael Garduno, Alexander Gill, Samantha Gruenheid, Linda Harris, Carol B. Huang, Hongsheng Huang, Roger Johnson, Yann Joly, Maud Kerhoas, Nguyet Kong, Gisèle Lapointe, Line Larivière, Stéphanie Loignon, Danielle Malo, Sylvain Moineau, Walid Mottawea, Kakali Mukhopadhyay, Céline Nadon, John Nash, Ida Ngueng Feze, Dele Ogunremi, Ann Perets, Ana V. Pilar, Aleisha R. Reimer, James Robertson, John Rohde, Kenneth E. Sanderson, Lingqiao Song, Roger Stephan, Sandeep Tamber, Paul Thomassin, Denise Tremblay, Valentine Usongo, Caroline Vincent, Siyun Wang, Joel T. Weadge, Martin Wiedmann, Lucas Wijnands, Emily D. Wilson, Thomas Wittum, Catherine Yoshida, Khadija Youfsi, Lei Zhu, Bart C. Weimer, Lawrence Goodridge, Roger C. Levesque |
Abstract |
The Salmonella Syst-OMICS consortium is sequencing 4,500 Salmonella genomes and building an analysis pipeline for the study of Salmonella genome evolution, antibiotic resistance and virulence genes. Metadata, including phenotypic as well as genomic data, for isolates of the collection are provided through the Salmonella Foodborne Syst-OMICS database (SalFoS), at https://salfos.ibis.ulaval.ca/. Here, we present our strategy and the analysis of the first 3,377 genomes. Our data will be used to draw potential links between strains found in fresh produce, humans, animals and the environment. The ultimate goals are to understand how Salmonella evolves over time, improve the accuracy of diagnostic methods, develop control methods in the field, and identify prognostic markers for evidence-based decisions in epidemiology and surveillance. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 50% |
Canada | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 103 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 21 | 20% |
Student > Ph. D. Student | 15 | 15% |
Student > Master | 15 | 15% |
Professor | 10 | 10% |
Student > Bachelor | 5 | 5% |
Other | 16 | 16% |
Unknown | 21 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 33 | 32% |
Biochemistry, Genetics and Molecular Biology | 10 | 10% |
Immunology and Microbiology | 9 | 9% |
Economics, Econometrics and Finance | 4 | 4% |
Nursing and Health Professions | 3 | 3% |
Other | 13 | 13% |
Unknown | 31 | 30% |